Simplifying ETL Resource Management with AWS Glue Auto Scaling

Overview

Modern data pipelines often deal with unpredictable workloads. Some days you might process gigabytes of data, while on others, you may need to handle terabytes. Traditionally, this meant over-provisioning resources “just in case,” which often led to wasted compute costs. To solve this challenge, AWS Glue Auto Scaling automatically adjusts compute resources based on workload demand, ensuring you pay only for what you need while keeping jobs performant.

In this blog, we will explore what AWS Glue Auto Scaling is, how it works, its benefits, and how you can enable and monitor it effectively.

AWS Glue Auto Scaling

AWS Glue Auto Scaling allows your ETL, streaming, and interactive jobs to scale out (add more workers) dynamically or scale in (remove idle workers) depending on workload needs. Instead of guessing how many workers a job will require, you define a maximum number of workers, and AWS Glue automatically provisions resources up to that limit.

This feature is available for Glue version 3.0 and later, and supports multiple job types, including:

• ETL jobs (batch processing).
• Interactive sessions (for development/debugging in notebooks).
• Streaming jobs (real-time data processing).

Why Use Auto Scaling?

Here are some key advantages of enabling Auto Scaling in AWS Glue:

1. Cost Savings

Instead of paying for idle workers, you only pay for actively used workers. Auto Scaling minimizes resource wastage by dynamically adjusting executor counts.

2. Better Performance

Large data bursts are handled more smoothly because AWS Glue allocates additional executors when needed. When the workload drops, unused workers are released.

3. Less Manual Effort

You don’t need to predict resource requirements for every job. Just set the maximum number of workers, and AWS Glue will manage scaling automatically.

4. Efficient Resource Utilization

Amazon CloudWatch metrics like workerUtilization show that jobs typically run with 75–100% efficiency when Auto Scaling is enabled, compared to as low as 20–40% when fixed executors are used.

How do you enable auto scaling in AWS Glue?

You can enable Auto Scaling using the AWS Console, CLI/SDK, or interactive sessions.

1. Enabling Auto Scaling in Glue Studio (Console)

• Open your AWS Glue job in Glue Studio.
• Go to the Job details tab.
• Select AWS Glue version 3.0 or later.
• Check the option to scale the number of workers automatically.
• Enter the Maximum number of workers allowed for that job.

2. Enabling Auto Scaling in Interactive Sessions

When using Jupyter notebooks with AWS Glue, you can enable Auto Scaling with:

This turns on Auto Scaling and enables Amazon CloudWatch logging for observability.

3. Auto Scaling for Streaming Jobs

For streaming workloads, AWS Glue evaluates multiple micro-batches before scaling. If you want finer control, you can add the following argument:

This allows scaling within each micro-batch for faster responsiveness.

Monitoring Auto Scaling

Enabling Auto Scaling is just the start, you also need visibility into how resources are used. AWS Glue offers several ways to monitor scaling behaviour:

• Amazon CloudWatch Metrics

executor allocation with metrics like:

• driver.ExecutorAllocationManager.executors.numberAllExecutors
• driver.ExecutorAllocationManager.executors.numberMaxNeededExecutors
• AWS Glue Studio Monitoring Dashboard

Provides visual insights on DPU hours consumed, making it easier to compare jobs with and without Auto Scaling enabled.

• Spark UI

Let’s you see when executors are added or removed during a job run.

• Amazon CloudWatch Logs

Especially useful in interactive sessions where logs tagged with “executor” show scaling events in real time.

Common Use Cases

1. Batch Jobs with Variable Data

Perfect for workloads that change daily (for example, different sales volumes on weekdays vs. weekends).

2. Multi-Stage Jobs

Some stages require more executors than others; Auto Scaling dynamically adjusts executor counts to match each stage’s demand.

3. Driver-Heavy Workloads

For jobs where certain tasks run mostly on the driver, executors are only scaled up when needed, saving costs.

4. IP-Constrained VPCs

Auto Scaling limits the number of active workers, reducing pressure on VPC IP allocations.

Conclusion

AWS Glue Auto Scaling helps organizations reduce costs and improve efficiency by dynamically matching compute resources with workload needs.

By enabling this feature, businesses gain a smarter, more cost-effective way to manage ETL and streaming pipelines.

Drop a query if you have any questions regarding AWS Glue Auto Scaling and we will get back to you quickly.

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